- Hands-on technical leader in the AI CoE to architect, build, integrate, and run production-grade AI solutions (Core ML + GenAI + Agentic) across multiple initiatives. You will technically manage/mentor junior AI/ML engineers, engage stakeholders/clients, and ensure solutions meet enterprise-grade standards (security, reliability, governance).

What Youll Do :

- Architect & deliver end-to-end AI-enabled software solutions : Core ML/DL, GenAI (LLMs, RAG), and agentic workflows, integrated into existing products.

- Lead engineering execution across multiple projects : design reviews, code reviews, technical decisions, and mentoring junior engineers.

- Enterprise integration : build AI capabilities into legacy/enterprise apps using APIs, microservices, event-driven patterns, queues/streams, and secure integration approaches.

- Stakeholder & client engagement : define AI use cases with business/external stakeholders; lead PoCs; contribute to solution proposals / RFPs .

- MLOps/DevOps ownership : CI/CD for apps + models, containerization, release automation, model registry, monitoring, and rollback strategies.

- Process & governance : deliver under Agile/SAFe/Waterfall, produce HLD/LLD, and follow compliance, data governance, and change-control processes.

- CoE initiatives : build reusable components, reference architectures, internal libraries, standards, and accelerators.

What You Bring (Must-Have) :

- Strong software engineering fundamentals : Python + solid backend engineering; ability to integrate with UI layers (web apps) as needed.

- Hands-on AI/ML delivery : model development + deployment experience across traditional ML and deep learning, plus GenAI patterns (RAG, prompt engineering, evals; fine-tuning is a plus).

- Experience building agentic / tool-using LLM systems (or equivalent orchestration patterns) in real implementations.

- Cloud proficiency in one : AWS or Azure or GCP (compute, storage, networking, security; managed ML services a plus).

- DevOps/MLOps : Docker, Kubernetes, CI/CD; ML lifecycle tooling such as MLflow/Kubeflow/SageMaker/Azure ML (or equivalent).

- Working knowledge of enterprise data platforms / ETL pipelines.

- Exposure to enterprise monitoring/ticketing (e.g., Splunk/Datadog/AppDynamics; Jira/ServiceNow) or similar operational toolchains.

- Experience working in secure/regulatory environments and adhering to data governance (PII, access controls, auditability).